XSurfer: Reconstructing surface meshes of cerebral and cerebellar cortex from diverse MRI data using untrained neural networks

Cortical surface reconstruction (CSR) is widely used in neuroimag-ing and is crucial for quantitative analyses of cerebral cortical thickness andsulcal morphology. While CSR is a mature technology when applied routinelyto adult T1 -weighted brain MRI data of the cerebrum, it remains underexploredfor a broad range of MRI contrasts, resolutions, ages, species, and brain struc-tures such as the cerebellum. To address this challenge, we propose XSurfer,a contrast- and resolution-agnostic CSR framework that performs optimizationon single images using an untrained neural network such that training data arenot needed. Specifically, XSurfer starts from segmentations of 3D MRI data,applies sulcal cerebrospinal fluid (CSF) recovery in sulci to address limitationscaused by partial-volume effects, identifies CSF voxels in deep sulci, and extractstopologically correct initial white matter (WM) surfaces and pseudo-target pialsurfaces. It then employs an untrained neural network to predict velocity fields,which are driven by mesh-based and geometrically-constrained loss functionterms to achieve diffeomorphic surface deformation, thereby reconstructing accu-rate pial surfaces. We evaluated XSurfer across different contrasts, resolutionsand seven datasets spanning adult, fetal, infant, ex vivo human, and non-humanprimate brain; XSurfer provides accurate CSR for all cases, surpassing state-of-the-art learning-based methods and recon-all-clinical recently providedby FreeSurfer as a more general-purpose CSR method. By enabling precise,contrast- and resolution-agnostic CSR without pretraining, XSurfer facilitatescross-domain multi-modal neuroimaging and standardizes morphometric analysisfor neuroscience research and clinical applications. The source code is publiclyavailable at: https://github.com/birthlab/XSurfer.